Conventional hyperspectral imaging sensors are configured to have sensor pixels that are approximately the same size or slightly larger than the point spread function of the sensor at most or all of the wavelengths captured. The point spread function describes the spreading or blurring of a point source in a captured image caused by the response of the imaging system to the point source. As a result, conventional hyperspectral imaging sensors may have relatively large physical pixels. Large physical pixels, however, have high solid angle-area products and receive a large etendue which limits the resolution of the sensor. As a consequence, the ground sample distance captured by a pixel may be too large for use in surveillance applications, for example.
Conventional hyperspectral imaging sensors, e.g., space-based sensors, tend to require large apertures, which increases the cost of the imaging system. In particular, the cost for space-based and airborne platforms, for example, may scale geometrically with the size of the aperture. Further, conventional processing algorithms used with conventional hyperspectral imaging sensors tend to produce inferior results when the energy from a point target is blurred over several pixels.
Accordingly, conventional hyperspectral imaging sensors and processing algorithms are inadequate for certain applications which may require detecting point targets blurred over multiple pixels and/or having a size of one pixel or less. Further, these applications may also require higher area coverage, resolution, and signal-to-noise ratio, and a smaller aperture than conventional systems can provide. Therefore, hyperspectral imaging sensors and processing algorithms having greater performance are desired.